diff --git a/strax/utils.py b/strax/utils.py
index df13d9bf..49971807 100644
--- a/strax/utils.py
+++ b/strax/utils.py
@@ -808,8 +808,8 @@ def convert_tuple_to_list(init_func_input):
 
 @export
 def convert_structured_array_to_df(structured_array, log=None):
-    """
-    Convert a structured numpy array to a pandas DataFrame.
+    """Convert a structured numpy array to a pandas DataFrame.
+
     Parameters:
     structured_array (numpy.ndarray): The structured array to be converted.
     Returns:
@@ -821,7 +821,7 @@ def convert_structured_array_to_df(structured_array, log=None):
         import logging
 
         log = logging.getLogger("strax_array_to_df")
-    
+
     data_dict = {}
     converted_cols = []
     for name in structured_array.dtype.names:
diff --git a/tests/test_core.py b/tests/test_core.py
index c09cb907..3ce990b3 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -34,7 +34,7 @@ def test_core(allow_multiprocess, max_workers, processor):
 
 @processing_conditions
 def test_core_df(allow_multiprocess, max_workers, processor, caplog):
-    """Test that get_df works with N-dimensional data"""
+    """Test that get_df works with N-dimensional data."""
     mystrax = strax.Context(
         storage=[],
         register=[Records, Peaks],
@@ -47,7 +47,10 @@ def test_core_df(allow_multiprocess, max_workers, processor, caplog):
     p = mystrax.get_single_plugin(run_id, "records")
     assert len(df.loc[0, "data"]) == 200
     assert len(df) == p.config["recs_per_chunk"] * p.config["n_chunks"]
-    assert "contain non-scalar entries. Some pandas functions (e.g., groupby, apply) might not perform as expected on these columns." in caplog.text
+    assert (
+        "contain non-scalar entries. Some pandas functions (e.g., groupby, apply) might not perform as expected on these columns."
+        in caplog.text
+    )
 
 
 def test_post_office_state():